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Analysis of the shift from 2012 to 2016
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group.mean <- function(x, group) { | |
out <- tapply(x, group, mean, na.rm = TRUE) | |
out[group] | |
} | |
data(state.fips, package = "maps") | |
state.fips <- unique(state.fips[,c("fips","abb")]) | |
state.fips$abb <- as.character(state.fips$abb) | |
state.fips <- rbind(state.fips, c(2, "AK")) | |
state.fips <- rbind(state.fips, c(15, "HI")) | |
rownames(state.fips) <- state.fips$abb | |
fips.state <- state.fips | |
rownames(fips.state) <- fips.state$fips | |
# From the ACES 2014-2010 | |
# C23002A SEX BY AGE BY EMPLOYMENT STATUS FOR THE POPULATION 16 YEARS AND OVER (WHITE ALONE) | |
unemp <- read.csv("~/workland/data/census/unemp-white-acs1410-C23002A.csv", stringsAsFactors = FALSE) | |
unemp$w.unemp2014 <- with(unemp, (m.unemp.1664 + m.unemp.65p + f.unemp.1664 + f.unemp.65p)) | |
unemp$w.labfor2014 <- with(unemp, (m.labfor.1664 + m.labfor.65p + f.labfor.1664 + f.labfor.65p)) | |
unemp$w.unemp.rate2014 <- unemp$w.unemp2014/unemp$w.labfor2014 | |
countydata <- unemp[, c("fips", "w.unemp.rate2014")] | |
countydata$stfips <- as.numeric(substr(countydata$fips, start = 1, stop = nchar(countydata$fips)-3)) | |
countydata$state.abb <- fips.state[as.character(countydata$stfips), "abb"] | |
## Census 2010 (Table DP-1) | |
census2010 <- read.csv("~/workland/data/census-tables/DEC_10_SF1_P9.csv") | |
census2010$totpop2010 <- census2010$D001 | |
census2010$whtot2010 <- census2010$D005 | |
census2010$fips <- census2010$GEO.id2 | |
countydata <- merge(countydata, census2010[, c("fips", "whtot2010", "totpop2010")], by = "fips", all.x = TRUE) | |
countydata$whprop2010 <- countydata$whtot2010/countydata$totpop2010 | |
## Election returns from uselectionatlas.org | |
gen2016 <- read.csv("~/workland/data/2016_0_0_2.csv", skip = 1) | |
gen2012 <- read.csv("~/workland/data/2012_0_0_2.csv", skip = 1) | |
gen2012$obama.votes2012 <- gen2012$vote1 | |
gen2012$obama.share2012 <- gen2012$vote1/gen2012$totalvote | |
gen2012$romney.share2012 <- gen2012$vote2/gen2012$totalvote | |
gen2012$totalvote2012 <- gen2012$totalvote | |
vars2012 <- c("fips", "obama.share2012", "romney.share2012", "totalvote2012", "obama.votes2012") | |
gen2016$clinton.votes2016 <- gen2016$vote1 | |
gen2016$trump.share2016 <- gen2016$vote2/gen2016$totalvote | |
gen2016$clinton.share2016 <- gen2016$vote1/gen2016$totalvote | |
gen2016$johnson.share2016 <- gen2016$vote4/gen2016$totalvote | |
gen2016$totalvote2016 <- gen2016$totalvote | |
vars <- c("fips", "trump.share2016", "clinton.share2016", "totalvote2016", "clinton.votes2016") | |
countydata <- merge(countydata, gen2016[,vars], by = "fips", all.x = TRUE) | |
countydata <- merge(countydata, gen2012[,vars2012], by = "fips", all.x = TRUE) | |
countydata$unemp.stmean <- group.mean(countydata$w.unemp.rate2014, countydata$state.abb) | |
countydata$w.unemp.st.demean <- countydata$w.unemp.rate2014-countydata$unemp.stmean | |
countydata$clinton.obama.change <- countydata$clinton.share2016-countydata$obama.share2012 | |
countydata$co.diff.stmean <- group.mean(countydata$clinton.obama.change, countydata$state.abb) | |
countydata$clinton.obama.st.demean <- countydata$clinton.share2016-countydata$obama.share2012 - countydata$co.diff.stmean | |
rust.belt <- c("OH", "MI", "PA", "WI", "IN", "IL") | |
countydata$rust.belt <- 1 * (countydata$state.abb %in% rust.belt) | |
wh.counties <- countydata[which(countydata$whprop2010 > 0.95),] | |
my.cols <- ifelse(state.abb %in% rust.belt, adjustcolor("indianred", alpha = 0.5), adjustcolor("dodgerblue", alpha = 0.25)) | |
par(mar = c(5, 10, 1, 1)) | |
plot(clinton.obama.st.demean ~ w.unemp.st.demean, data = wh.counties, pch = 19, col = my.cols, xlab = "White Unemployment Rate, 2014 (State-demeaned)", cex = 0.75, bty = "n", ylab = "") | |
mtext(side = 2, las = 1, text = "Clinton-Obama\nVoteshare\nDifference\n(State-Demeaned)", line = 6, adj = 0.5) | |
out1 <- loess(clinton.obama.st.demean ~ w.unemp.st.demean, data = wh.counties, subset = rust.belt == 0) | |
j <- order(out1$x) | |
pred <- predict(out1, se = TRUE) | |
lines(x=out1$x[j], y = out1$fitted[j], col = "blue", lwd = 3) | |
##lines(x=out1$x[j], y = out1$fitted[j] + 2*pred$se.fit[j], col = "blue", lty = 2) | |
##lines(x=out1$x[j], y = out1$fitted[j] - 2*pred$se.fit[j], col = "blue", lty = 2) | |
out1 <- loess(clinton.obama.st.demean ~ w.unemp.st.demean, data = wh.counties, subset = rust.belt == 1) | |
j <- order(out1$x) | |
pred <- predict(out1, se = TRUE) | |
lines(x=out1$x[j], y = out1$fitted[j], col = "indianred", lwd = 3) | |
##lines(x=out1$x[j], y = out1$fitted[j] + 2*pred$se.fit[j], col = "indianred", lty = 2) | |
##lines(x=out1$x[j], y = out1$fitted[j] - 2*pred$se.fit[j], col = "indianred", lty = 2) | |
text(x = -0.06, y = 0.09, "Rust Belt", col = "indianred") | |
text(x = -0.07, y = 0.01, "All other\nstates", col = "dodgerblue") | |
## fixed effect regression (hide the FEs) | |
out <- lm(I(clinton.share2016-obama.share2012) ~ w.unemp.rate2014 + state.abb, data = wh.counties) | |
sout <- summary(out)$coefficients | |
sout[c(1,2),] | |
## fixed effect regression w/ interaction (hide the FEs) | |
out.inter <- lm(I(clinton.share2016-obama.share2012) ~ w.unemp.rate2014*rust.belt + state.abb, data = wh.counties) | |
sout.inter <- summary(out.inter)$coefficients | |
sout.inter[c(1,2, nrow(sout.inter)),] |
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